Hybrid model and appearance based eye tracking with kinect

Existing gaze estimation methods rely mainly on 3D eye model or 2D eye appearance. While both methods have validated their effectiveness in various fields and applications, they are still limited in practice, such as portable and non-intrusive system and robust eye gaze tracking in different environments. To this end, we investigate on combining eye model with eye appearance to perform gaze estimation and eye gaze tracking. Specifically, unlike traditional 3D model based methods which rely on cornea reflections, we plan to retrieve 3D information from depth sensor (Eg, Kinect). Kinect integrates camera sensor and IR illuminations into one single device, thus enable more flexible system settings. We further propose to utilize appearance information to help the basic model based methods. Appearance information can help better detection of gaze related features (Eg, pupil center). Plus, eye model and eye appearance can benefit each other to enable robust and accurate gaze estimation.